Practical Quantum Research Articles

Generalization metrics for practical quantum advantage in generative models

Generalization metrics for practical quantum advantage in generative models

A scalable cavity-based spin–photon interface in a photonic integrated circuit

A central challenge in quantum networking is transferring quantum states between different physical modalities, such as between flying photonic qubits and stationary quantum memories. One implementation entails using spin–photon interfaces that combine solid-state spin qubits, such as color centers in diamond, with photonic nanostructures. However, while high-fidelity spin–photon interactions have been demonstrated on isolated devices, building practical quantum repeaters requires scaling to large numbers of interfaces yet to be realized. Here, we demonstrate integration of nanophotonic cavities containing tin-vacancy (SnV) centers in a photonic integrated circuit (PIC). Out of a six-channel quantum microchiplet (QMC), we find four coupled SnV-cavity devices with an average Purcell factor of ∼7. Based on system analyses and numerical simulations, we find with near-term improvements this multiplexed architecture can enable high-fidelity quantum state transfer, paving the way toward building large-scale quantum repeaters.

Splitting and parallelizing of quantum convolutional neural networks for learning translationally symmetric data

The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable problems. However, the QCNN requires a large number of measurements for data learning, limiting its practical applications in large-scale problems. To alleviate this requirement, we propose an architecture called split-parallelizing QCNN (sp-QCNN), which exploits the prior knowledge of quantum data to design an efficient model. This architecture draws inspiration from geometric quantum machine learning and targets translationally symmetric quantum data commonly encountered in physics and quantum computing science. By splitting the quantum circuit based on translational symmetry, the sp-QCNN can substantially parallelize the conventional QCNN without increasing the number of qubits and improve the measurement efficiency by an order of the number of qubits. To demonstrate its effectiveness, we apply the sp-QCNN to a quantum phase recognition task and show that it can achieve comparable classification accuracy to the conventional QCNN while considerably reducing the measurement resources required. Due to its high measurement efficiency, the sp-QCNN can mitigate statistical errors in estimating the gradient of the loss function, thereby accelerating the learning process. These results open up possibilities for incorporating the prior data knowledge into the efficient design of QML models, leading to practical quantum advantages. Published by the American Physical Society 2024

Harnessing quantum plexcitons for enhanced sensitivity and selectivity of creatinine sensor

Despite their widespread usage, surface plasmon resonance (SPR) sensors have several drawbacks, including limited sensitivity and the requirement for sophisticated functionalization chemistry to enhance their performance. In contrast, using quantum states for sensing may result in an enormous increase in sensor performance. These states, however, often need a rigorously controlled environment, which limits their adoption. Recently, there has been a surge of interest in employing quantum plexcitons to solve this conundrum, which has recently been proven to be resilient and robust even in ambient conditions. Here, we report the utilization of plexcitons induced through hybridization of surface plasmon and excitons in a SPR-Otto configuration for sensing creatinine molecules. Using an organic pentamer, BOBzBT2, the ultra-strong coupling regime with a Rabi splitting of 22 % of the uncoupled energy was observed and exploited to monitor the creatinine concentrations. This demonstration proposes a way to combine the benefits of an SPR-based design with the sensitivity offered by quantum plexcitons, which is beneficial for future practical quantum sensing.

Enhancing quantum annealing in digital–analog quantum computing

Digital–analog quantum computing (DAQC) offers a promising approach to addressing the challenges of building a practical quantum computer. By efficiently allocating resources between digital and analog quantum circuits, DAQC paves the way for achieving optimal performance. We propose an algorithm designed to enhance the performance of quantum annealing. This method employs a quantum gate to estimate the goodness of the final annealing state and find the ground state of combinatorial optimization problems. We explore two strategies for integrating the quantum annealing circuit into the DAQC framework: (1) state preparation, and (2) embedding within the quantum gate. While the former strategy does not yield performance improvements, we discover that the latter enhances performance within a specific range of annealing time. Algorithms demonstrating enhanced performance utilize the imaginary part of the inner product of two states from different quantum annealing settings. This measure reflects not only the energy of the classical cost function but also the trajectory of the quantum dynamics. This study provides an example of how processing quantum data using a quantum circuit can outperform classical data processing, which discards quantum information.

Inverse Volume Scaling of Finite-Size Error in Periodic Coupled Cluster Theory

Coupled cluster theory is one of the most popular post-Hartree-Fock methods for molecular quantum chemistry. The finite-size error of the correlation energy in periodic coupled cluster calculations for three-dimensional insulating systems has been observed to satisfy the inverse volume scaling, even in the absence of any correction schemes. This is surprising, as simpler theories that utilize only a subset of the coupled cluster diagrams exhibit much slower decay of the finite-size error, which scales inversely with the length of the system. In this study, we review the current understanding of finite-size error in quantum chemistry methods for periodic systems. We introduce new tools that elucidate the mechanisms behind this phenomenon in the context of coupled cluster doubles calculations. This reconciles some seemingly paradoxical statements related to finite-size scaling. Our findings also show that singularity subtraction can be a powerful method to effectively reduce finite-size errors in practical quantum chemistry calculations for periodic systems. Published by the American Physical Society 2024

Improving the performance of practical phase-matching quantum key distribution with advantage distillation

Improving the performance of practical phase-matching quantum key distribution with advantage distillation

Topologically Protected Quantum Logic Gates with Valley-Hall Photonic Crystals.

Topological photonics provide a promising way to realize more robust optical devices against some defects and environmental perturbations. Quantum logic gates are fundamental units of quantum computers, which are widely used in future quantum information processing. Thus, constructing robust universal quantum logic gates is an important way forward to practical quantum computing. However, the most important problem to be solved is how to construct the quantum-logic-gate-required 2×2 beam splitter with topological protection. Here, the experimental realization of the topologically protected contradirectional coupler is reported, which can be employed to realize the quantum logic gates, including control-NOT and Hadamard gates, on the silicon photonic platform. These quantum gates not only have high experimental fidelities but also exhibit a certain degree of tolerances against certain types of defects. This work paves the way for the development of practical optical quantum computations and signal processing.

Atomically precise engineering of spin–orbit polarons in a kagome magnetic Weyl semimetal

Atomically precise defect engineering is essential to manipulate the properties of emerging topological quantum materials for practical quantum applications. However, this remains challenging due to the obstacles in modifying the typically complex crystal lattice with atomic precision. Here, we report the atomically precise engineering of the vacancy-localized spin–orbit polarons in a kagome magnetic Weyl semimetal Co3Sn2S2, using scanning tunneling microscope. We achieve the step-by-step repair of the selected vacancies, leading to the formation of artificial sulfur vacancies with elaborate geometry. We find that that the bound states localized around these vacancies undergo a symmetry dependent energy shift towards Fermi level with increasing vacancy size. As the vacancy size increases, the localized magnetic moments of spin–orbit polarons become tunable and eventually become itinerantly negative due to spin–orbit coupling in the kagome flat band. These findings provide a platform for engineering atomic quantum states in topological quantum materials at the atomic scale.

Side-channel security of practical quantum key distribution

We present general security proof for quantum key distribution (QKD) with imperfect states in whole space including both the operational space and the other space (side-channel space). We show that in a protocol where Alice (Bob) uses two states, the secure key rate for a real protocol using imperfect (mixed) states is the same as that of a perfect protocol if the real protocol can be mapped from the perfect protocol. The calculation of the secure key rate for imperfect sources is reduced to the lower bound of projection probability to vacuum for each state. This paves the operable way to side-channel-secure QKD under more realistic conditions, including imperfections of whole-space states, mixed states, and finite data size. Taking the sending-or-not-sending encoding as an example in calculation, we obtain good key rates and long secure distance for QKD that is both measurement-device-independent and secure with imperfect whole-space source states. Published by the American Physical Society 2024

Quantum Propulsion: Background and Practical Applications

This is the first introduction of new and revolutionary aerospace engines and propulsion methods. Our organization is currently developing quantum propulsion systems and space vehicles that will be capable of flying with enormous speed (potentially reaching and exceeding the speed of light) and will have unseen-before manoeuvrability and lifting capacity. They will provide 100% crew protection from deadly sun and space radiation which is essential for safe deep space travel and manned space exploration. They will also provide spacecraft with protection against space particles. To date, we have managed to crack the main secret of practical quantum engine design and we are ready to develop it further into fully operational aerospace vehicles. Quantum propulsion systems are the only systems that can facilitate realistic prospects of space mining on the industrial scale and deep space colonisation, including colonisation of habitable planets in the future. The cost efficiency of this new technology is going to be enormous. Development and production costs of quantum aerospace vehicles compared to production costs of chemical fuel jet spacecraft allow to reduce price per kilo space launch ratio hundreds of times, making deep space exploration and commercialisation more accessible and practically feasible at last. Manufacturing and maintenance of quantum-propelled flying machines that can reach the age of our solar system within hours will be no more expensive than manufacturing jet planes or helicopters of the same size. Quantum propulsion systems are going to replace outdated chemical fuel rocket and jet engines in the near future and will become the mainstay of air travel and space exploration.

Single-photon-based quantum secure protocol for the socialist millionaires’ problem

The socialist millionaires' problem, emanating from the millionaires’ problem, allows two millionaires to determine whether they happen to be equally rich while remaining their riches undisclosed to each other. Most of the current quantum solutions to the socialist millionaires’ problem have lower efficiency and are theoretically feasible. In this paper, we introduce a practical quantum secure protocol for the socialist millionaires’ problem based on single photons, which can be easily implemented and manipulated with current technology. Our protocol necessitates the involvement of a semi-honest third party (TP) responsible for preparing the single-photon sequences and transmitting them to Alice who performs Identity or Hadamard operations on the received quantum sequences via her private inputs and the secret keys, producing new quantum sequences that are subsequently sent to Bob. Similarly, Bob encodes his private inputs into the received quantum sequences to produce new quantum sequences, which are then sent to TP. By conducting single-particle measurements on the quantum sequences received from Bob, TP can ascertain the equality of private inputs between Alice and Bob, and subsequently communicate the comparison result to them. To assess the feasibility, the proposed protocol is simulated on IBM Quantum Cloud Platform. Furthermore, security analysis demonstrates that our protocol can withstand attacks from outsiders, such as eavesdroppers, and from insider participants attempting to grab the private input of another participant.

Sampled subblock hashing for large-input randomness extraction

Randomness extraction is an essential postprocessing step in practical quantum cryptography systems. When statistical fluctuations are taken into consideration, the requirement of large-input data size could heavily penalize the speed and resource consumption of the randomness-extraction process, thereby limiting the overall system performance. In this work, we propose a sampled subblock hashing approach to circumvent this problem by randomly dividing the large-input block into multiple subblocks and processing them individually. Through simulations and experiments, we demonstrate that our method achieves an order-of-magnitude increase in system throughput while keeping the resource utilization low. Furthermore, our proposed approach is applicable to a generic class of quantum cryptographic protocols that satisfy the generalized entropy-accumulation framework, presenting a highly promising and general solution for high-speed postprocessing in quantum cryptographic applications such as quantum key distribution and quantum random number generation. Published by the American Physical Society 2024

Partially Fault-Tolerant Quantum Computing Architecture with Error-Corrected Clifford Gates and Space-Time Efficient Analog Rotations

Quantum computers are expected to drastically accelerate certain computing tasks versus classical computers. Noisy intermediate-scale quantum (NISQ) devices, which have tens to hundreds of noisy physical qubits, are gradually becoming available, but it is still challenging to achieve useful quantum advantages in meaningful tasks. On the other hand, full fault-tolerant quantum computing (FTQC) based on quantum error correction code remains far beyond realization due to its extremely large requirement of high-precision physical qubits. In this study, we propose a quantum computing architecture to close the gap between NISQ and FTQC architectures. Our architecture is based on erroneous arbitrary rotation gates and error-corrected Clifford gates implemented by lattice surgery. We omit the typical distillation protocol to achieve direct analog rotations and small qubit requirements, and minimize the remnant errors of the rotations by a carefully designed state injection protocol. Our estimation based on numerical simulations shows that for early-FTQC devices that consist of 104 physical qubits with physical error probability p=10−4, we can perform roughly 1.72×107 Clifford operations and 3.75×104 arbitrary rotations on 64 logical qubits. Such computations cannot be realized by the existing NISQ and FTQC architectures on the same device, as well as classical computers. We hope that our proposal and the corresponding development of quantum algorithms based on it will bring new insights into the realization of practical quantum computers in the future. Published by the American Physical Society 2024

Distributed quantum neural networks via partitioned features encoding

Quantum neural networks are expected to be a promising application in near-term quantum computing, but face challenges such as vanishing gradients during optimization and limited expressibility by a limited number of qubits and shallow circuits. To mitigate these challenges, an approach using distributed quantum neural networks has been proposed to make a prediction by approximating outputs of a large circuit using multiple small circuits. However, the approximation of a large circuit requires an exponential number of small circuit evaluations. Here, we instead propose to distribute partitioned features over multiple small quantum neural networks and use the ensemble of their expectation values to generate predictions. To verify our distributed approach, we demonstrate ten class classification of the Semeion and MNIST handwritten digit datasets. The results of the Semeion dataset imply that while our distributed approach may outperform a single quantum neural network in classification performance, excessive partitioning reduces performance. Nevertheless, for the MNIST dataset, we succeeded in ten class classification with exceeding 96% accuracy. Our proposed method not only achieved highly accurate predictions for a large dataset but also reduced the hardware requirements for each quantum neural network compared to a large single quantum neural network. Our results highlight distributed quantum neural networks as a promising direction for practical quantum machine learning algorithms compatible with near-term quantum devices. We hope that our approach is useful for exploring quantum machine learning applications.

Memory-induced weak dissipation in fractional-time-derivative quantum Lindblad-based model

Modeling the interaction between a quantum system and its environment is crucial for practical quantum technologies. The Lindblad master equation is the simplest equation to understand this interaction. In this paper, we have extended the traditional Lindblad equation by fractionalizing its time derivative to account for the memory-induced dissipation. Using this approach, we show weak dissipation of selected quantum systems can be reproduced by this proposed time-fractional Lindblad equation without introducing any specific dissipation terms in the model. By varying the order of the time-fractional Lindblad equation without dissipation terms, we can reproduce the results with good agreements to three tested cases: (a) dissipative Rabi oscillation, (b) dissipative Ising model, and (c) collapse and revival in the Jaynes–Cumming model. We believe this proposed time-fractional Lindblad equation may be a useful modeling tool to characterize weakly dissipative quantum systems in practical quantum technologies, especially if the complex dissipation mechanism is not completely known from the traditional approaches.

Decomposing large unitaries into multimode devices of arbitrary size

Decomposing complex unitary evolution into a series of constituent components is a cornerstone of practical quantum information processing. While the decomposition of an n×n unitary into a product of 2×2 subunitaries (which can for example be realized by beam splitters and phase shifters in linear optics) is well established, we show how for any m>2 this decomposition can be generalized into a product of m×m subunitaries (which can then be realized by a more complex device acting on m modes). If the cost associated with building each m×m multimode device is less than constructing with m(m−1)2 individual 2×2 devices, we show that the decomposition of large unitaries into m×m submatrices is more resource efficient and exhibits a higher tolerance to errors, than its 2×2 counterpart. This allows larger-scale unitaries to be constructed with lower errors, which is necessary for various tasks, not least boson sampling, the quantum Fourier transform, and quantum simulations. Published by the American Physical Society 2024

A framework for demonstrating practical quantum advantage: comparing quantum against classical generative models

Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over an existing framework for evaluating the generalization performance of generative models, and we establish the first quantitative comparative race towards practical quantum advantage (PQA) between classical and quantum generative models, namely Quantum Circuit Born Machines (QCBMs), Transformers (TFs), Recurrent Neural Networks (RNNs), Variational Autoencoders (VAEs), and Wasserstein Generative Adversarial Networks (WGANs). After defining four types of PQAs scenarios, we focus on what we refer to as potential PQA, aiming to compare quantum models with the best-known classical algorithms for the task at hand. We let the models race on a well-defined and application-relevant competition setting, where we illustrate and demonstrate our framework on 20 variables (qubits) generative modeling task. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models. Such a feature is highly desirable in a wide range of real-world applications where the available data is scarce.

Practical high-dimensional quantum key distribution protocol over deployed multicore fiber

Quantum key distribution (QKD) is a secure communication scheme for sharing symmetric cryptographic keys based on the laws of quantum physics, and is considered a key player in the realm of cyber-security. A critical challenge for QKD systems comes from the fact that the ever-increasing rates at which digital data are transmitted require more and more performing sources of quantum keys, primarily in terms of secret key generation rate. High-dimensional QKD based on path encoding has been proposed as a candidate approach to address this challenge. However, while proof-of-principle demonstrations based on lab experiments have been reported in the literature, demonstrations in realistic environments are still missing. Here we report the generation of secret keys in a 4-dimensional hybrid time-path-encoded QKD system over a 52-km deployed multicore fiber link forming by looping back two cores of a 26-km 4-core optical fiber. Our results indicate that robust high-dimensional QKD can be implemented in a realistic environment by combining standard telecom equipment with emerging multicore fiber technology.

Shaping photons: Quantum information processing with bosonic cQED

With its rich dynamics, the quantum harmonic oscillator is an innate platform for understanding real-world quantum systems and could even excel as the heart of a quantum computer. A particularly promising and rapidly advancing platform that harnesses quantum harmonic oscillators for information processing is the bosonic circuit quantum electrodynamics (cQED) system. In this article, we provide perspectives on the progress, challenges, and future directions in building a bosonic cQED quantum computer. We describe the main hardware building blocks and how they facilitate quantum error correction, metrology, and simulation. We conclude with our views of the key challenges that lie on the horizon, as well as scientific and cultural strategies for overcoming them and building a practical quantum computer with bosonic cQED hardware.